Educational Values in Different Social-Economic Status—A Study Case of Six Families in Maros Regency
Bibliographic record
Abstract
This research aims at determining educational values in families by describing the priority of the values selected in the educational value, the role of parents, and the socialization of values that were used by parents in educational values. This research is a case study with a qualitative approach. The subjects of the research were selected purposively. The data were collected by observation, interview, and documentation. The results showed that families in different socioeconomic status choose religious values as the most important priority value to be implanted in children, followed by academic value, economic value and social value. Parents in low and medium socioeconomic status have different roles, father as a breadwinner and the mother served as a teacher of children at home who prepares internal needs of the family. Family in higher socioeconomic status does not have a clear division of roles, because the parents educate children at home and make a living for the family's economic needs together. The method of value socialization that parents used is advice, storytelling, dialogue, exemplary, punishment, and awards. Exemplary method is considered as the most effective method of social values in educational value. Punishment is the ultimate alternative punishment, is only done if the children are lazy to worship or severe violations such as stealing or violating immoral.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".